Feature Request: Support custom and non-square input sizes

[j99ca]

According to the docs, the input sizes supported by OTX is a set list of square input sizes. With most convolutional model architectures, it should be possible to use non-square input sizes while maintaining the use of pre-trained weights, through a global pooling layer at the head of the model. This is possible with some classification models in TensorHub, and it would be a great feature for OTX classification and would accelerate our adoption of this library at the edge. I have use cases for very tall images from certain sensors where resizing them to any of the set list of square sizes skews the aspect ratio and can destroy the features needed for classification.

[goodsong81]

@eunwoosh Let's consider non-square input size.

[j99ca]

@goodsong81 @eunwoosh do you folks have a timeline for custom inputs (with or without non-square inputs) in this library? I am trying to schedule some integration into OTX 2.x and the lack of this feature is blocking.

Keep up the good work!

[goodsong81]

@goodsong81 @eunwoosh do you folks have a timeline for custom inputs (with or without non-square inputs) in this library? I am trying to schedule some integration into OTX 2.x and the lack of this feature is blocking.

Keep up the good work!

Not yet confirmed but I suppose it will be enabled in the next quarter (Q3) of this year.